Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

TL;DR

This paper explores Bayesian adaptive designs for multi-arm Phase II clinical trials, enabling dynamic treatment comparison and early stopping based on posterior probability assessments, with extensions for time-to-event data.

Contribution

It introduces a Bayesian framework for adaptive treatment allocation and selection, including early stopping rules, primarily for binary outcomes, with discussions on extending to time-to-event data.

Findings

Bayesian posterior-based treatment comparison

Adaptive treatment stopping rules

Extensions for time-to-event data

Abstract

Clinical trials are an instrument for making informed decisions based on evidence from well-designed experiments. Here we consider adaptive designs mainly from the perspective of multi-arm Phase II clinical trials, in which one or more experimental treatments are compared to a control. Treatment allocation of individual trial participants is assumed to take place according to a fixed block randomization, albeit with an important twist: The performance of each treatment arm is assessed after every measured outcome, in terms of the posterior distribution of a corresponding model parameter. Different treatments arms are then compared to each other, according to pre-defined criteria and using the joint posterior as the basis for such assessment. If a treatment is found to be sufficiently clearly inferior to the currently best candidate, it can be closed off either temporarily or permanently from further participant accrual. The latter possibility provides a method for adaptive treatment selection, including early stopping of the trial. The main development in the paper is in terms of binary outcomes, but some extensions, notably for handling time-to-event data, are discussed as well. The presentation is to a large extent comparative and expository.